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Record W6926024227 · doi:10.20380/gi2022.27

Tutorials for Children by Children: Design and Evaluation of a Children's Tutorial Authoring Tool for Digital Art

2022· article· en· W6926024227 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCanada Human-Computer Communications Society · 2022
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCreativityWorkflowSoftwareDigital artCitizen journalismFlexibility (engineering)IncentiveParticipatory design

Abstract

fetched live from OpenAlex

Digital art tools allow children to express their creativity and can help them develop important skills. There are numerous software tutorials available to help teach and inspire digital art enthusiasts, however, most are authored for and by adults. Given that children are increasingly contributing online digital content, in this paper, we investigate a tutorial authoring design concept where children can capture their drawings and information on their process, with the long-term objective of allowing children to share both their creativity and their workflows with other children. Through participatory design sessions, prototyping, and an evaluation, we explore children's attitudes towards the creation of digital art tutorials, focusing on their perceived incentives to author such tutorials and how they feel about the concept of sharing their tutorials with other children. We also elicit reactions towards specific design elements. Our findings suggest important considerations for tools designed to motivate and support children's creation of digital art tutorials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.280
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it